Current HIV/SIV Reservoir Assays for Preclinical and Clinical Applications: Recommendations from the Experts 2022 NIAID Workshop Summary
Bibliographic record
Abstract
Since the first HIV-cured person was reported in 2009, a strong interest in developing highly sensitive HIV and SIV reservoir assays has emerged. In particular, the question arose about the comparative value of state-of-the-art assays to measure and characterize the HIV reservoir, and how these assays can be applied to accurately detect changes in the reservoir during efforts to develop a cure for HIV infection. Second, it is important to consider the impact on the outcome of clinical trials if these relatively new HIV reservoir assays are incorporated into clinical trial endpoints and/or used for clinical decision-making. To understand the advantages and limitations and the regulatory implications of HIV reservoir assays, the National Institute of Allergy and Infectious Diseases (NIAID) sponsored and convened a meeting on September 16, 2022, to discuss the state of knowledge concerning these questions and best practices for selecting HIV reservoir assays for a particular research question or clinical trial protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.162 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".